Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Bifurcation control deals with modification of bifurcation characteristics of a parameterized nonlinear system by a designed control input. Typical bifurcation control objectives include delaying t...

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Bifurcation control: theories, methods, and applications

A singularity-free terminal sliding mode (TSM) control scheme with fast and fixed-time convergence for a class of second-order non-linear systems with matched uncertainties and external disturbances is proposed. A novel singularity-free fast TSM (SFTSM) structure is constructed and the upper-bound of convergence time is independent of initial states and can be set arbitrarily in advance. Then, the proposed SFTSM controller is designed by combining the SFTSM structure and the composite fast reaching law and the globally fixed-time stability is guaranteed and derived with the phase plane analysis and Lyapunov stability theory. Finally, the simulation results for a single inverted pendulum tracking system are included to verify the effectiveness of the proposed control method.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2016.1457

On SFTSM control with fixed-time convergence

Computing the centroid of an interval type-2 fuzzy set (IT2 FS) is an important operation in a type-2 fuzzy logic system (where it is called type-reduction), but it is also a potentially time-consuming operation. In this paper, an enhanced opposite direction searching (EODS) algorithm is presented for doing this. The EODS comes from an early version of the IT2 FS type-reduction method called the opposite direction searching (ODS) algorithm, which has been proven faster than the most commonly used Enhanced Karnik-Mendel (EKM) method. The EODS differs from the ODS in two high speed formulas for calculating the centroid endings. Quantitative analysis on the mathematical operations and comparisons performed by EODS, ODS, and EKM algorithms shows that EODS could save about 50% of the calculations and comparisons in relation to ODS. Compared with EKM, it could save about 67% to 80% of the calculations and comparisons. Simulation experiments have been performed to compare EODS with the ODS and EKM methods in terms of average CPU time. The experimental results validate the quantitative analysis.

Advantages of the Enhanced Opposite Direction Searching Algorithm for Computing the Centroid of An Interval Type‐2 Fuzzy Set

Fixed-time control for spacecraft attitude tracking based on quaternion

Aero-optic imaging deviation is a kind of aero-optic effect. It characterizes the image position displacement on an imaging plane. This paper studies the influence of altitude on aero-optic imaging deviation. The Reynolds-averaged Navier-Stokes solver provided in FLUENT was used for flow computations. The Runge-Kutta method based ray tracing was adopted for optics calculations. The orthogonal array was brought in for the experiment arrangement. Four representative suites of imaging deviations and imaging deviation slopes were obtained in the altitude range of 10-60 km. The results show that as altitude increases, the imaging deviation decreases, and the imaging deviation slope approaches zero from a negative value.

Influence of altitude on aero-optic imaging deviation

This paper proposes a nonsingular terminal sliding mode control scheme with fast fixed-time convergence for a class of second-order nonlinear systems in the presence of matched uncertainties and perturbations. First, based on fixed-time stability theory, a novel stable system is proposed. Then, using the fixed-time stable system, a fast fixed-time nonsingular terminal sliding surface is derived. The settling time is independent of the initial system state and can be set in advance with the design parameters; the upper-bound of convergence time is derived from the Lyapunov theory. Moreover, the proposed control scheme has an advantage in convergence rate over existing results and achieves better control performance with low control energy cost. The simulation results for a tracking system with a single inverted pendulum are presented to validate the effectiveness and superiority of the proposed control method.

/pdf/a-fast-nonsingular-terminal-sliding-mode-control-method-for-4tag6ammp1.pdf

Yuanli Cai

Papers

On SFTSM control with fixed-time convergence

Advantages of the Enhanced Opposite Direction Searching Algorithm for Computing the Centroid of An Interval Type‐2 Fuzzy Set

Fixed-time control for spacecraft attitude tracking based on quaternion

Influence of altitude on aero-optic imaging deviation

A Fast Nonsingular Terminal Sliding Mode Control Method for Nonlinear Systems With Fixed-Time Stability Guarantees